Applications of Human Tissue-Engineered Blood Vessel Models to Study the Effects of Shed Membrane Microparticles from T-Lymphocytes on Vascular Function
Bibliographic record
Abstract
Microparticles (MPs) are membrane vesicles harboring cell surface proteins and containing cytoplasmic components of the original cell. High levels of circulating MPs have been detected in pathological states associated with vascular dysfunction. We took advantage of the self-assembly method of tissue engineering to produce in vitro three vascular constructs from human vascular smooth muscle cells and fibroblasts to investigate the role of the adventitia in the modulation of vascular tone by MPs, comparing the contractile response of each of these constructs to histamine. The first two were composed of an adventitia (tissue-engineered vascular adventitia (TEVA)) or a media (tissue-engineered vascular media (TEVM)) solely, and the third one contained a media and an adventitia (tissue-engineered vascular media and adventitia (TEVMA)). In the three constructs, the results show that histamine induces contraction insensitive to blockade of inducible nitric oxide (NO) synthase (iNOS) and cyclooxygenase-2 (COX-2) and not affected by MP treatment. MPs decreased NO production and nuclear factor (NF)-kappaB expression but did not affect superoxide anion (O(2)(-)) release in TEVA. MPs enhanced NF-kappaB expression but did not affect iNOS and COX-2 expression or NO or O(2)(-) release in TEVM. In TEVMA, MPs did not enhance NF-kappaB expression, but COX-2 expression was higher, and O(2)(-) release was lower. Thus, MPs affected NO, O(2)(-), NF-kappaB, and COX-2 in a subtle fashion to maintain the contractile response to histamine. The use of tissue-engineered vascular constructs results in a better understanding of the effect of MPs on human adventitia and media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".